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紧急医疗服务人员在为社区服务时面临的暴力风险。

Emergency Medical Service Personnel's Risk From Violence While Serving the Community.

作者信息

Maguire Brian J, O'Neill Barbara J

机构信息

Brian J. Maguire is with the School of Health, Medical, and Applied Sciences, Central Queensland University, North Rockhampton, Queensland, Australia. Barbara J. O'Neill is with the School of Nursing, Midwifery, and Social Sciences, Central Queensland University.

出版信息

Am J Public Health. 2017 Nov;107(11):1770-1775. doi: 10.2105/AJPH.2017.303989. Epub 2017 Sep 21.

Abstract

OBJECTIVES

To determine the risks of violence-related injury among emergency medical services (EMS) personnel in the United States.

METHODS

We analyzed 1630 violence-related occupational injury cases reported to the US Bureau of Labor Statistics for the years 2012 to 2015 and conducted secondary searches within the Bureau of Labor Statistics Web site.

RESULTS

The number of cases per year varied between 250 and 560. Perpetrators included patients (77%) and coworkers (8%). Female EMS personnel had a disproportionately greater risk of violence-related injuries. The most common (35%) injury type was "sprains-strains-tears"; about 4% of the assault cases resulted in fractures, 13% resulted in surface wounds, and 190 were head injuries. About a third of the cases were classified as intentional.

CONCLUSIONS

The findings indicate a clear need for reliable interventions. The differences in risk for women indicate that some interventions may need to be demographic-specific. Because of the limitations of national data, future researchers will need access to agency-level data that include hours worked and call volume by demographic factors such as gender.

摘要

目的

确定美国紧急医疗服务(EMS)人员遭受与暴力相关伤害的风险。

方法

我们分析了2012年至2015年期间向美国劳工统计局报告的1630起与暴力相关的职业伤害案例,并在劳工统计局网站上进行了二次搜索。

结果

每年的案例数量在250至560之间。施暴者包括患者(77%)和同事(8%)。女性EMS人员遭受与暴力相关伤害的风险 disproportionately 更高。最常见(35%)的伤害类型是“扭伤-拉伤-撕裂伤”;约4%的袭击案例导致骨折,13%导致体表伤口,190起为头部受伤。约三分之一的案例被归类为故意行为。

结论

研究结果表明显然需要可靠的干预措施。女性风险的差异表明某些干预措施可能需要针对特定人群。由于国家数据的局限性,未来的研究人员需要获取机构层面的数据,这些数据应包括按性别等人口因素划分的工作时长和呼叫量。

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